Biology subjects

Bonn, S.

Publications and source records attributed to Bonn, S..

31 records · Page 2Linked to original sources

bioRxiv2022

Orphan nuclear receptors Err2 and 3 promote a feature-specific terminal differentiationprogram underlying gamma motor neuron function and proprioceptive movement control

Motor neurons are commonly thought of as mere relays between the central nervous system and the movement apparatus, yet, in mammals about one-third of them function exclusively as regulators of muscle proprioception. How these gamma motor neurons acquire properties to function differently from the muscle force-producing alpha motor neurons remains unclear. Here, we found that upon selective loss of the orphan nuclear receptors Err2 and Err3 (Err2/3) in mice, gamma motor neurons acquire characteristic structural (e.g. synaptic wiring), but not functional (e.g. physiological firing rates) properties necessary for regulating muscle proprioception, thus disrupting gait and precision movements in vivo. Moreover, Err2/3 operate via transcriptional activation of neural activity modulators, one of which (Kcna10) promoted gamma motor neuron functional properties. Our work identifies a long-sought mechanism specifying gamma motor neuron properties necessary for proprioceptive movement control, which implies a feature-specific terminal differentiation program implementing neuron subtype-specific functional but not structural properties. SummaryThe transcription factors Err2 and 3 promote functional properties in a subset of motor neurons necessary for executing precise movements.

neuroscience↗

bioRxiv2022

Pathogen Detection in RNA-Seq Data with Pathonoia

MotivationRecent evidence suggests that bacterial and viral infections may cause or exacerbate many human diseases. One method of choice to detect microbes in tissue is RNA sequencing. While the detection of specific microbes using RNA sequencing offers good sensitivity and specificity, untargeted approaches suffer from very high false positive rates and a lack of sensitivity for lowly abundant organisms. ResultsWe introduce Pathonoia, an algorithm that detects viruses and bacteria in RNA sequencing data with high precision and recall. Pathonoia first applies an established k-mer based method for species identification and then aggregates this evidence over all reads in a sample. In addition, we provide an easy-to-use analysis framework that highlights potential microbe-host cell interactions by correlating the microbial to host gene expression. Pathonoia outperforms competing algorithms in microbial detection specificity, both on in silico and real datasets. Lastly, we present two case studies in human liver and brain in which microbial infection might exacerbate disease. AvailabilityA Python package for Pathonoia sample analysis and a guided analysis Jupyter notebook for bulk RNAseq datasets are available on GitHub https://github.com/kepsi/Pathonoia. Contactaliebho1@jhu.edu and s.bonn@uke.de Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/476681v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@16775f2org.highwire.dtl.DTLVardef@17099f4org.highwire.dtl.DTLVardef@18b8dfeorg.highwire.dtl.DTLVardef@117498b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Simultaneous pathway engagement of translation in astrocytes, circadian rhythm in GABAergic neurons and cytoskeleton in glutamatergic neurons precede electroencephalographic changes in neurodegenerative prion disease

Selective vulnerability is an enigmatic feature of neurodegenerative diseases (NDs), whereby a widely expressed protein causes lesions in specific brain regions and cell types. This selectivity may arise from cells possessing varying capacities to regain proteostasis when stressed by cytotoxic protein conformers. Using the RiboTag method in mice, translational responses of five neural subtypes to acquired prion disease (PrD) were measured. Pre-onset and disease onset timepoints were chosen based on longitudinal electroencephalography (EEG) that revealed a gradual increase in theta power between 10- and 18-weeks after prion injection, resembling a clinical feature of human PrD. At disease onset, marked by significantly increased theta power and histopathological lesions, mice had pronounced translatome changes in all five cell types despite having a normal outward appearance. Remarkably, at a pre-onset stage, prior to EEG and neuropathological changes, we found that 1) translatomes of astrocytes indicated a sharply reduced synthesis of ribosomal and mitochondrial components, 2) excitatory neurons showed increased expression of cytoskeletal genes, and 3) inhibitory neurons revealed reduced expression of circadian rhythm network genes. Further assessment for the role of circadian rhythms using a jet lag paradigm modestly exacerbated disease. These data demonstrate that early translatome responses to neurodegeneration emerge prior to other signs of disease and are unique to different cell types. Therapeutic strategies may need to target multiple pathways, each in specific populations of cells, early in the disease process.

neuroscience↗

bioRxiv2021

Neuronal cholesterol synthesis is essential for repair of chronically demyelinated lesions in mice

Astrocyte-derived cholesterol supports brain cells under physiological conditions. However, in demyelinating lesions, astrocytes downregulate cholesterol synthesis and the cholesterol that is essential for remyelination has to originate from other cellular sources. Here, we show that repair following acute versus chronic demyelination involves distinct processes. In particular, we found that in chronic myelin disease, when recycling of lipids is often defective, de novo neuronal cholesterol synthesis is critical for regeneration. By gene expression profiling, genetic loss of function experiments and comprehensive phenotyping, we provide evidence that neurons increase cholesterol synthesis in chronic myelin disease models and MS patients. In mouse models, neuronal cholesterol facilitated remyelination specifically by triggering OPC proliferation. Our data contribute to the understanding of disease progression and have implications for therapeutic strategies in MS patients.

neuroscience↗

bioRxiv2020

Integrated multi-omics analysis reveals common and distinct dysregulated pathways for genetic subtypes of Frontotemporal Dementia

Understanding the molecular mechanisms underlying frontotemporal dementia (FTD) is essential for the development of successful therapies. Systematic studies on human post-mortem brain tissue of patients with genetic subtypes of FTD are currently lacking. The Risk and Modyfing Factors of Frontotemporal Dementia (RiMod-FTD) consortium therefore has generated a multi-omics dataset for genetic subtypes of FTD to identify common and distinct molecular mechanisms disturbed in disease. Here, we present multi-omics datasets generated from the frontal lobe of post-mortem human brain tissue from patients with mutations in MAPT, GRN and C9orf72 and healthy controls. This data resource consists of four datasets generated with different technologies to capture the transcriptome by RNA-seq, and small RNA-seq, and supplemented this date with CAGE-seq, and methylation profiling. We show concrete examples on how to use the resulting data and confirm current knowledge about FTD and identify new processes for further investigation. This extensive multi-omics dataset holds great value to reveal new research avenues for this devastating disease.

neuroscience↗

bioRxiv2020

Bias invariant RNA-seq metadata annotation

Recent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Here we investigate RNA-seq metadata prediction based on gene expression values. We present a deep-learning based domain adaptation algorithm for the automatic annotation of RNA-seq metadata. We show how our algorithm outperforms existing approaches as well as traditional deep learning methods for the prediction of tissue, sample source, and patient sex information across several large data repositories. By using a model architecture similar to siamese networks the algorithm is able to learn biases from datasets with few samples. Our domain adaptation approach achieves metadata annotation accuracies up to 12.3% better than a previously published method. Lastly, we provide a list of more than 10,000 novel tissue and sex label annotations for 8,495 unique SRA samples.

bioinformatics↗

bioRxiv2020

Deep learning-based molecular morphometrics for kidney biopsies

Morphologic examination of tissue biopsies is essential for histopathological diagnosis. However, accurate and scalable cellular quantification in human samples remains challenging. Here, we present a deep learning-based approach for antigen-specific cellular morphometrics in human kidney biopsies, which combines indirect immunofluorescence imaging with U-Net-based architectures for image-to-image translation and dual segmentation tasks, achieving human-level accuracy. In the kidney, podocyte loss represents a hallmark of glomerular injury and can be estimated in diagnostic biopsies. Thus, we profiled over 27,000 podocytes from 110 human samples, including patients with anti-neutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA-GN), an immune-mediated disease with aggressive glomerular damage and irreversible loss of kidney function. Previously unknown morphometric signatures of podocyte depletion were identified in patients with ANCA-GN, which allowed patient classification and showed potential for risk stratification in combination with routine clinical tools. Together, our approach enables robust and scalable molecular morphometric analysis of human tissues, yielding deeper biological insights into the human kidney pathophysiology. SummaryDeep learning enables robust and scalable molecular morphometric analysis of human tissues, yielding deeper biological insights into the human kidney pathophysiology.

pathology↗

bioRxiv2019

Deep-learning-based cell composition analysis from tissue expression profiles.

We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single cell RNA-seq data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple data sets. Due to this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scadens comprehensive software package is easy to use on novel as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

bioinformatics↗

bioRxiv2018

Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks

A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here we propose the use of conditional single cell Generative Adversarial Neural Networks (cscGANs) for the realistic generation of single cell RNA-seq data. cscGANs learn non-linear gene-gene dependencies from complex, multi cell type samples and use this information to generate realistic cells of defined types. Augmenting sparse cell populations with cscGAN generated cells improves downstream analyses such as the detection of marker genes, the robustness and reliability of classifiers, the assessment of novel analysis algorithms, and might reduce the number of animal experiments and costs in consequence. cscGANs outperform existing methods for single cell RNA-seq data generation in quality and hold great promise for the realistic generation and augmentation of other biomedical data types.

bioinformatics↗

bioRxiv2017

SEA: The Small RNA Expression Atlas

We present the Small RNA Expression Atlas (SEA), a web application that allows for the interactive querying, visualization, and analysis of known and novel small RNAs across ten organisms. It contains sRNA and pathogen expression information for over 4,200 published samples with standardized search terms and ontologies. In addition, SEA allows for the interactive visualization and re-analysis of 879 differential expression and 514 classification comparisons. SEAs user model enables sRNA researchers to compare and re-analyze user-specific and published datasets, highlighting common and distinct sRNA expression patterns.\n\nWe provide evidence for SEAs fidelity by (i) generating a set of 591 tissue specific miRNAs across 30 tissues, (ii) finding known and novel bacterial and viral infections across diseases, and (iii) determining a Parkinsons disease-specific blood biomarker signature using novel data.\n\nWe believe that SEAs simple semantic search interface, the flexible interactive reports, and the user model with rich analysis capabilities will enable researchers to better understand the potential function and diagnostic value of sRNAs or pathogens across tissues, diseases, and organisms.\n\nAvailability and ImplementationSEA is implemented in Java, J2EE, spring, Django, html5, css3, JavaScript, Bootstrap, Vue.js, D3, mongodb and neo4j. It is freely available at http://sea.ims.bio/.

bioinformatics↗

bioRxiv2017

Oasis2.0: improved online analysis of small RNA-seq data

Oasis 2 is a new main release of the Oasis web application for the detection, differential expression, and classification of small RNAs in deep sequencing data. Compared to its predecessor Oasis, Oasis 2 features a novel and speed-optimized sRNA detection module that supports the identification of small RNAs in any organism with higher accuracy. Next to the improved detection of small RNAs in a target organism, the software now also recognizes potential cross-species miRNAs and viral and bacterial sRNAs in infected samples. In addition, novel miRNAs can now be queried and visualized interactively, providing essential information for over 700 high-quality miRNA predictions across 14 organisms. Robust biomarker signatures can now be obtained using the novel enhanced classification module. Oasis 2 enables biologists and medical researchers to rapidly analyze and query small RNA deep sequencing data with improved precision, recall, and speed, in an interactive and user-friendly environment.\n\nAvailability and Implementation: Oasis 2 is implemented in Java, J2EE, mysql, Python, R, PHP and JavaScript. It is freely available at http://oasis.dzne.de

bioinformatics↗

bioRxiv2017

The Landscape Of Human Mutually Exclusive Splicing

Mutually exclusive splicing of exons is a mechanism of functional gene and protein diversification with pivotal roles in organismal development and diseases such as Timothy syndrome, cardiomyopathy and cancer in humans. In order to obtain a first genome-wide estimate of the extent and biological role of mutually exclusive splicing in humans we predicted and subsequently validated mutually exclusive exons (MXEs) using 515 publically available RNA-seq datasets. Here, we provide evidence for the expression of over 855 MXEs, 42% of which represent novel exons, increasing the annotated human mutually exclusive exome more than five-fold. The data provides strong evidence for the existence of large and multi-cluster MXEs in higher vertebrates and offers new insights into MXE splicing mechanics and evolution. Finally, MXEs are significantly enriched in pathogenic mutations and their spatio-temporal expression predicts human disease pathology.

bioinformatics↗

bioRxiv2017

Genetics of educational attainment aid in identifying biological subcategories of schizophrenia

Higher educational attainment (EA) is negatively associated with schizophrenia (SZ). However, recent studies found a positive genetic correlation between EA and SZ. We investigated possible causes of this counterintuitive finding using genome-wide association study results for EA and SZ (N = 443,581) and a replication cohort (1,169 controls; 1,067 cases) with deeply phenotyped SZ patients. We found strong genetic dependence between EA and SZ that cannot be explained by chance, linkage disequilibrium, or assortative mating. Instead, several genes seem to have pleiotropic effects on EA and SZ, but without a clear pattern of sign concordance. Genetic heterogeneity of SZ contributes to this finding. We demonstrate this by showing that the polygenic prediction of clinical SZ symptoms can be improved by taking the sign concordance of loci for EA and SZ into account. Furthermore, using EA as a proxy phenotype, we isolate FOXO6 and SLITRK1 as novel candidate genes for SZ.

genetics↗